Deep convolutional neural networks, or deep CNNs, are at the center of remarkable development in a deep learning field. The CNNs were already in use in the 1990s for solving character recognition problems, but they have become widely used thanks to results of recent studies. Such a deep CNN won the championship outdoing other competitors in the ImageNet Classification in 2012. Afterward, the CNNs have become a very useful tool in the field of machine learning.
Image segmentation is a method for generating a label image by partitioning an image into objects of interest, e.g., a vehicle, a human being, a road, the sky, a building, etc. Recently, as the deep learning technology has become popular, image segmentation is frequently used for deep learning.
The conventional image segmentation by using such deep learning techniques is a process of generating feature maps by applying several convolution operations to a training image using multiple convolutional layers of the CNN, then generating label images by applying several deconvolution operations to the feature maps using multiple deconvolutional layers of the CNN, acquiring loss values by comparing the acquired label images with a ground truth (GT) of the training image, and then learning the deconvolutional layers and the convolutional layers by using the acquired loss values. After that, label images of objects of interest in a test image inputted are obtained by using the learned deconvolutional layers and the learned convolutional layers.
However, such conventional image segmentation method segments the test image by using a CNN with parameters having been learned by using the training image. Therefore, when the test image whose state of a scene is radically different from that of a scene in the training image is being segmented, the segmentation results may be inaccurate. For example, when an image including a road is being segmented, the shape of the road may be different for each country, and therefore, without learning of such a specific situation, the road could fail to be segmented accurately.
As can be seen from the above, under the conventional image segmentation method, regardless of the state of the scene from which the test image for segmentation was acquired, the test image was segmented by using a pre-learned CNN. Therefore, the method failed in adapting to change in the state of the scene from which the test image for segmentation was acquired.